RUNNING HEAD: DRD4, PRIORITY ITEMS, AND ATTENTION
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DRD4 Long Allele Carriers Show Heightened Attention to High-Priority Items
Relative to Low-Priority Items
Marissa A. Gorlick*a, Darrell A. Worthy*b, Valerie S. Knopikc John E. McGearyd,
Christopher G. Beeversa and W. Todd Maddoxa1
a
University of Texas at Austin Psychology Department
b
c
d
Texas A&M Psychology Department
Rhode Island Hospital & Brown University
Providence Veterans Affairs Medical Center
*The two first authors contributed equally.
In press Journal of Cognitive Neuroscience
Abstract
Humans with 7 or more repeats in exon III of the DRD4 gene (long DRD4 carriers)
sometimes demonstrate impaired attention, as seen in ADHD, and at other times
demonstrate heightened attention, as seen in addictive behavior. Though the clinical
effects of DRD4 are the focus of much work, this gene may not necessarily serve as a
‘risk’ gene for attentional deficits, but as a plasticity gene where attention is heightened
for priority items in the environment and impaired for minor items. Here we examine the
role of DRD4 in two tasks that benefit from selective attention to high-priority
information. We examine a category learning task where performance is supported by
focusing on features and updating verbal rules. Here selective attention to the most
salient features is associated with good performance. In addition, we examine the
Operation Span Task (OSPAN), a working memory capacity task that relies on selective
attention to update and maintain items in memory while also performing a secondary
task. Long DRD4 carriers show superior performance relative to short DRD4
homozygotes (six or less tandem repeats) in both the category learning and OSPAN tasks.
These results suggest that DRD4 may serve as a ‘plasticity’ gene where individuals with
the long allele show heightened selective attention to high-priority items in the
environment, which can be beneficial in the appropriate context.
Keywords: DRD4, attention, working memory, category-learning, learning, salience
1
This study was funded by a NIDA grant DA032457 to WTM and CGB. We would like to thank
Alex Kline, Bailey Cain, Katherine Bedford, Kayla Beaucage and Seth Koslov for their help with data
collection. Thanks to Jessica Cooper for helpful comments. The views expressed in this article are those
of the authors and do not necessarily reflect the position or policy of the Department of Veterans Affairs.
RUNNING HEAD: DRD4, PRIORITY ITEMS, AND ATTENTION
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DRD4 Long Allele Carriers Show Heightened Attention to High-Priority Items
Relative to Low-Priority Items
The DRD4 gene codes a post-synaptic D4 dopamine receptor primarily transcribed
in the prefrontal cortex (Oak, Oldenhof, & Van Tol, 2000; Wells, Beevers, Knopik, &
McGeary, 2012) where the variable number of tandem repeats in exon III are involved in
modulating attention. Interestingly, the presence of an allele with seven or more repeats
(long) has been associated with both disrupted and heightened attention. For example,
the long allele has been associated with increased incidence of ADHD with impaired
executive attention (Bidwell et al., 2011; Gizer & Waldman, 2012; Gizer, Ficks, &
Waldman, 2009; Hutchison et al., 2003; Kustanovich et al., 2003; Langley et al., 2004;
Laucht, becker, Blomeyer, & Schmidt, 2007; Laucht, becker, El-Faddagh, Hohm, &
Schmidt, 2005; Manor et al., 2001; Munafò, Yalcin, Willis-Owen, & Flint, 2008;
Swanson et al., 2000). However, other work has found that long DRD4 allele carriers
demonstrated heightened executive attention for emotional stimuli (Wells et al., 2012)
and smoking related cues (Hutchison et al., 2003; Laucht et al., 2005; 2007; Munafò et
al., 2008). Thus, prior work has been mixed regarding the role of the DRD4 in
modulating attention.
It has been proposed that DRD4 behaves as a plasticity gene rather than a
vulnerability gene (Belsky & Pluess, 2009; Oak et al., 2000; Wells et al., 2012). Under
this view, long allele carriers are not simply more susceptible to adverse environmental
stimuli, rather their cognitions are perturbed to a greater extent by environmental factors
generating greater changes in behavior. These changes could be advantageous or
disadvantageous depending on the task. One possibility is that long allele carriers show
heightened attention to high-priority items and impaired attention to low-priority items in
the environment.
Biased competition theory suggests that frontal brain structures are critical for
goal-directed biases (Beck & Kastner, 2009; Bidwell et al., 2011; Gizer et al., 2009;
Gizer & Waldman, 2012; Kustanovich et al., 2003; Langley et al., 2004; Lee, Itti, &
Mather, 2012; Manor et al., 2001; Swanson et al., 2000). Here goal-relevant information
is held in working memory and used to enhance attention for priority targets amid
distractors. For example, when searching for a friend’s face in a crowd, their high-priority
facial features are stored in working memory systems where reciprocal projections to the
visual cortex enhance similar features. As DRD4 is primarily transcribed in the prefrontal
cortex (Oak et al., 2000; Wells et al., 2012), we hypothesize that genetic polymorphisms
change the way goal-directed priorities are selected which would influence performance
across multiple cognitive domains. In line with this theory, common top-down priority
maps have been demonstrated in the prefrontal and parietal cortices across several
cognitive domains including working memory and attention (Hutchison et al., 2003; Ikkai
& Curtis, 2011; Laucht et al., 2005; 2007; Munafò et al., 2008). Together with prior
research on the long allele, we predict a DRD4 long-allele advantage in tasks that require
goal-directed attention to high-priority items in a complex environment.
We test this hypothesis by examining DRD4 in non-clinical populations
performing two tasks where greater plasticity for high-priority items and reduced
plasticity for low-priority items improves subsequent learning and memory. First we
RUNNING HEAD: DRD4, PRIORITY ITEMS, AND ATTENTION
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examine a category learning in a task where stimuli that differ along ten binary
dimensions must be classified. Previous research has found that narrow goal-directed
attention to a small number of features supports learning in this task and computational
models are available to determine how much attentional weight is given to each feature
(Gorlick & Maddox, 2013). We predict long allele carriers will show heightened
attention to salient features relative to short homozygotes and, as a result, demonstrate
superior task performance. We also predict impaired attention for minor features in long
allele carriers relative to short homozygotes.
Though the category learning paradigm is ideal for mapping improved and
impaired attention within the same study, it does not demonstrate whether these effects
are driven by bottom-up or top-down processes. DRD4 is primarily expressed in the
prefrontal cortex, thus one nuance of our hypothesis is that these effects are likely driven
by top-down goal directed processes. To evaluate whether these effects hold during a task
where bottom-up attentional competition isn’t important for performance, we also
examine whether long allele carriers show superior performance in a complex sequential
task that requires sustained attention to a primary, demanding working-memory task amid
distraction, the Operations Span task (OSPAN; Conway et al., 2005; Engle, Tuholski, &
Conway, 1999). Here the high-priority goal is to accurately recall sequences of letters
while simultaneously performing a distracting secondary task. Performance in this task is
dependent on goal-directed attentional biases for the priority letter-span task in the face
of interference (Conway et al., 2005; Conway, Cowan, & Bunting, 2001; Kane, Bleckley,
Conway, & Engle, 2001). We predict that long allele carriers will outperform short
carriers due to heightened top-down attention to the letters that must be remembered on
each trial.
Method
Participants
Replication is an important step in determining whether single nucleotide
polymorphism effects will be consistent across studies and populations. To improve our
confidence in the generalizability of these effects, we have included data from two
samples. One sample was an unscreened pool of participants (general sample) that
completed a shorter version of the category learning task consisting of one block of
training and test. The second sample was screened for clinical, neuropsychological and
personality differences described below. As the screened sample has fewer potential
confounds, they completed more rigorous testing with two blocks of the category
learning tasks as well as the memory span task.
General Sample
Participants aged 18-35 were recruited from the University of Texas at Austin
introductory participant pool (127 female, 60 male). Participants were not screened for
clinical symptoms or neuropsychological differences during recruitment.
Screened Sample
Participants aged 18-35 were recruited from the greater Austin Community
through fliers and newspaper ads (Overall: 125 female, 96 male). The samples for the
screened category-learning and OSPAN tasks were largely overlapping (92.8%) within
this group depending on task exclusions and available data. To rule out anyone that had
significant psychiatric disease, potential participants were screened via telephone using
RUNNING HEAD: DRD4, PRIORITY ITEMS, AND ATTENTION
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the Mini International Neuropsychiatric Interview (MINI), which screened for 17
different Axis I Diagnostic and Statistical Manual of Mental Disorders-IV (DSM-IV)
disorders, including alcohol and drug abuse and/or dependence and attention-deficit
hyperactivity disorder (ADHD). The MINI has acceptable validity, test-retest, and interrater reliability (Lecrubier et al., 1997; D. V. Sheehan, Lecrubier, & Sheehan, 1998).
Participants who met criteria for a current or past psychiatric diagnosis (determined by
the MINI), currently taking psychoactive medication, currently in psychotherapy, or with
a history of brain trauma were excluded from the study. Excluded participants were
offered referrals to local mental health clinics.
Category Learning Task
Cartoon animals constructed from 10 binary features such as head orientation (up
or forward), body color (grey or yellow), and tail (thin or thick) served as stimuli from a
total of 210 = 1024 possible stimuli (Figure 1A). Due to varied contrast and characteristic
differences between dimensions, some are more salient than others. For each participant
one stimulus was selected at random to represent the A prototype. The B prototype has
the opposite value on each feature. Category stimuli were derived by distorting the
prototypes on one to four randomly selected features. Exemplars that differ from the
prototype on five features were not used as they are ambiguous. Thus, each of 10 features
is equally relevant, however some features are more salient than others due to bottom-up
visual differences such as contrast which influences priority during learning.
Screened sample participants completed 2 blocks of training and test and general
sample participants completed 1 block of training and test. Training consisted of 20 trials
followed by a test phase with 42 novel stimuli including the prototypes and equal
numbers of A and B items (Figure 1B). During training, participants were shown 10 A
and 10 B items in a random order, generated a response (self paced with 1000ms between
items) by pressing the relevant key on the keyboard and were given corrective feedback
for 2000ms. Within each category, 2 training stimuli were randomly selected that differed
from the category prototype on 1 feature, 3 differed on 2 features, 3 differed on 3 features
and 2 differed on 4 features.
A 42-trial test phase followed training that included each prototype and 5 stimuli
that differed from each prototype on 1, 2, 3 and 4 features. On each test trial, after
stimulus onset, the participant was prompted to give an A or B response by pressing the
relevant key on the keyboard with no corrective feedback. Responses were self-paced
with 1000ms between items. For the block 1 analysis, those that were non-learners (less
than 40% accuracy in block 1; 6 long carriers and 12 short homozygotes) were excluded.
For the block 2 analysis, those that were non-learners (less that 40% accuracy in block 2,
2 long carriers and 2 short homozygotes) were excluded (Table 1). From the screened
sample, 16 participants were not included due to incomplete or unsaved data.
To gain a more complete understanding these effects across different samples, we
examine early learning (block 1) in an unscreened sample (General Sample) and replicate
these effects in a sample that has been screened for mental illness and matched using
neuropsychological measures (Screened Sample). Next, we take a closer look at learning
with more experience (block 2) in the screened sample. Prior work suggests that we
should see larger effects in later learning due to differences in learning rates.
OSPAN Task
RUNNING HEAD: DRD4, PRIORITY ITEMS, AND ATTENTION
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In the Operation Span task (OSPAN, Figure 1C), participants’ primary goal is to
remember a sequence of letters presented on a computer screen while also performing
distracting secondary modular arithmetic problems. Further, letters are presented
sequentially reducing bottom-up influences on attention. Thus, recalling the letters has
high goal-directed priority and the arithmetic problems have low goal-directed priority.
After each span, participants are asked to recall, in order, the sequence of letters that was
just presented. The primary goal is to correctly recall each sequence of letters, but they
must also maintain at least 85% accuracy on the math problems. This has been used as a
domain-general measure of executive attention as well as working memory (Conway et
al., 2005). The task consisted of 15 recall phases where participants were asked to recall
spans of between 3-7 letters. Participants were asked to recall a total of three spans of
each span length (3-7 letters) for a total of 75 letters. From the overall sample, 25
participants were not included due to incomplete or unsaved data. Participants were
excluded if they did not maintain an accuracy level of at least 85% on the arithmetic
problems (3 short homozygotes; Table 1).
Importantly, there are not items that are more salient or ‘attention-grabbing’ in the
task, making it ideal for testing whether the effects seen during Category Learning are
simply due to heightened attention to bottom-up salient items in the environment or due
to goal-directed heightened attention to high-priority stimuli.
Genetics
The 48 bp VNTR in the DRD4 was assayed using previously reported methods
(Hutchison, LaChance, Niaura, Bryan, & Smolen, 2002). The primer sequences used are
forward, 5’ AGGACCCTCATGGCCTTG-3’ (fluorescently labeled), and reverse, 5'GCGACTACGTGGTCTACTCG-3’ (Lichter et al., 1993). Alleles were visualized using
capillary electrophoresis. The 7 repeat allele is quite distinct from the 2–6 repeat alleles
and likely originated as a rare mutational event that became more frequent as a result of
positive selection (Ding et al., 2002). Participants were classified as DRD4 long (i.e.
homozygous or heterozygous for an allele of 7 or more repeats) or as DRD4 short carriers
(i.e. both alleles <7 repeats). Details on specific genetic combinations can be seen in
Table 2. For quality assurance purposes in the event of ambiguity in the genotyping, the
assay is run in duplicate or triplicate in order to verify the results.
For both the screened sample and the general sample, results of an exact test for
Hardy–Weinberg proportions using Markov chain-Monte Carlo implementation (Guo &
Thompson, 1992) indicate that our observed genotype frequencies, when looking at both
the entire sample and the Caucasian subset, differ significantly from Hardy–Weinberg
equilibrium (HWE; p<.00000).
Data Analysis
Category Learning Measures
In addition to overall accuracy at test, we were interested in fine-grained measures
of accuracy for exemplars that were more or less prototypical. To examine how similarity
to the prototype affected performance for DRD4 long and short homozygotes, we
computed average accuracy for similar test items (i.e., those that differed from the
prototype on 1 – 2 dimensions) and for dissimilar test items (i.e., those that differed from
the prototype on 3 – 4 dimensions).
We examine measures of overall accuracy for block 1 using 2 sample (general,
screened) X 2 DRD4 status (long carrier, short homozygote) ANOVA. We examine the
RUNNING HEAD: DRD4, PRIORITY ITEMS, AND ATTENTION
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effects of similarity on accuracy using a 2 sample (general, screened) X 2 DRD4 status
(long carrier, short homozygote) X 2 similarity to the prototype (similar, dissimilar)
repeated measures ANOVA. For block 2, which includes only the screened sample, we
conduct independent samples t-tests to examine overall accuracy and a 2 DRD4 status
(long carrier, short homozygote) X 2 similarity to the prototype (similar, dissimilar)
repeated measures ANOVA to examine similarity.
Computational Models
The attentional processes underlying category learning can be better understood
using computational models (Glass, Chotibut, Pacheco, Schnyer, & Maddox, 2012;
Gorlick & Maddox, 2013). These prototype models incorporate attention-weighted,
summed similarity between each item and the A and B prototypes to generate predicted
response probabilities on a trial-by-trial basis. Thus, it is possible to estimate the amount
of selective attention that was paid to each of the 10 binary features during learning.
Formally, the psychological distance between the test stimulus 𝑖 and the prototype 𝑃 is
given by Equation 1. Here k denotes the dimension (1 – 10) and wk denotes the attention
weight for the kth dimension (1 > 𝑤𝑘 > 0; sum to 1). Distances are converted to
similarities using the well-established exponential decay similarity function as seen in
Equation 2 (Shepard, 1987). Here c denotes the sensitivity (0 < c < 10). The predicted
probability of responding ‘A’ for test stimulus 𝑖 is calculated by Equation 3.
Differences in salience, such as contrast differences, can elevate a subset of
features to a higher priority creating biased attentional competition (Lee et al., 2012).
Though all 10 features are equally relevant to each exemplar’s category membership,
some features are more salient than others and tend to be selectively attended to across
participants. Importantly, salience differences in conjunction with computational models
that describe attention to each feature make the prototype learning task an ideal paradigm
to test our hypothesis as we are able to examine both impaired and improved attention
across salient and minor features within the same study.
Thus, to explore whether long allele carriers would demonstrate heightened
attention for the most salient high-priority features at the expense of the least salient lowpriority features, attention weights were examined. The most salient feature (most
frequent dimension with maximum attention weight across participants), and least salient
feature (least frequent dimension with maximum attention weight across participants) of
the 10 binary dimensions were computed and the group means compared using 2 sample
(general, screened) X 2 DRD4 status (long carrier, short homozygote) X 2 Feature
(minor, salient) ANOVA for block 1 and a 2 DRD4 status (long carrier, short
homozygote) X 2 Feature (minor, salient) ANOVA samples t-tests within the screened
sample for block 2.
Memory Span Measures
The traditional method of obtaining an individual’s operation span is to add the
length of each span that was correctly recalled to the individual’s score (Unsworth &
Engle, 2005). Thus, correctly recalling a span of seven letters adds seven points to one’s
score while incorrectly recalling even one letter in the span results in zero points being
added to one’s score. A second way of scoring the OSPAN is to simply compute the
proportion of correctly recalled letters. We explore both measures of performance in the
results using independent samples t-tests.
Potential Confounds
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It is important to carefully examine potential confounding factors such as the
distribution of racial and ethnic groups within the sample, differences in fluid cognitive
abilities, and personality differences that are associated with the behavior of interest
when exploring the relationship between genetic polymorphisms and behavior. To
measure these confounds, all participants completed a demographic form indicating their
race and ethnicity. They also responded to a series of computer-based questionnaires that
included the Center for Epidemiological Studies Depression Scale to assess dysphoric
mood which may bias the salience of rewards and punishment (CESD; Radloff, 1977)
and the Barrett Impulsiveness Scale to assess novelty seeking which has been
demonstrated to moderate the effects of DRD4 on attention in some studies (BIS-11;
Stanford et al., 2009).
A series of neuropsychological tests was also completed by the screened sample
only to assess differences in fluid cognitive abilities. The neuropsychological battery
includes the Wechsler Adult Intelligence Scale-Fourth Edition’s Digit Span as a measure
of working memory without interference from a secondary task and Vocabulary as a
measure of intelligence (WAIS-IV; Wechsler, 1981), the Stroop test as a measure
response inhibition (Stroop, 1935), and the Wechsler Memory Scale as a measure of
episodic memory (WMS-IV).
Neuropsychological results were collected for the screened sample only and
normalized for age and gender using standardized procedures and converted to Z-scores.
After each primary analysis, neuropsychological and personality factors that showed
significant differences between DRD4 long allele carriers and short homozygotes were
included in an ANCOVA model to determine whether these effects are robust to these
factors. Only those factors that demonstrated significant differences at p<.05 were
included to avoid over-parameterization. Results were also replicated within a Caucasian
subgroup to ensure that models were not significantly affected by possible population
stratification.
Results
Category Learning Results
Block 1: Screened Sample with General Sample Replication
Accuracy
To examine DRD4’s influence across screened and unscreened samples, we
conducted a 2 sample (screened, general) x 2 DRD4 (long carrier, short homozygote)
ANOVA on overall accuracy (Figure 2A). There was a main effect of DRD4 status where
long allele carriers (M = .73, SD = 11) demonstrated better overall block 1 accuracy than
short homozygotes (M = .70, SD = .12), F(1, 381) = 4.06 , p =.045 η2= .01. However,
independent samples t-tests within the general and screened sample were non-significant
(p<.2). There was no effect of sample nor interactions.
To examine DRD4’s influence across screened and unscreened samples, we
conducted a 2 sample (screened, general) X 2 DRD4 (long carrier, short homozygote) x 2
similarity (similar, dissimilar) repeated measures ANOVA (Figure 2B, 2C). There is a
main effect of DRD4 status, F(1, 381) = 4.26, p=.04, η2 = .01 where long allele carriers
are more accurate (M = .73, SD = .1) than short homozygotes (M = .70, SD = .12). In
addition, there was a main effect of similarity, F(1, 381) = 3.73, p<.001, η2 = .24 where
accuracy is higher for similar (M = .78, SD = .15) than dissimilar exemplars (M = .62, SD
= .12). There were no effects of sample or any other significant effects. The main effect
RUNNING HEAD: DRD4, PRIORITY ITEMS, AND ATTENTION
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of similarity held when decomposed in 2 DRD4 x 2 similarity ANOVAs within the
general, F(1, 185) = 191.45, p<.001, η2 = .25, and screened, F(1, 196) = 191.3, p<.001, η2
= .24, samples, however, the main effect of DRD4 was non significant within each
sample.
Computational Modeling: Salience and Attention
To determine whether attentional focus for salient and minor features depends on
DRD4 allele status across screened and general samples during early learning, we
conducted a 2 DRD4 (long carrier, short homozygote) X 2 sample (screened, general) X
2 feature (salient, minor) mixed effects ANOVA. There was a main effect of feature, F(1,
381)=7.55, p<.001, η2 = .1, where salient features have higher attentional weight than
minor features (MSalient = .15, SDSalient = .19; MMinor = .05, SDMinor = .08), and no other
significant effects. The main effect of featjre held in 2 DRD4 x 2 similarity ANOVAs
within the general, F(1, 185) = 44.92, p<.001, η2 = .11, and screened, F(1, 196) = 32.94,
p<.001, η2 = .09, samples.
Potential Confounds
In the general sample, analysis of individual differences revealed no significant
differences between DRD4 groups at the p<.05 level, however there was a marginal
difference in impulsivity as measured by the BIS-11 (p=.06) which was significant in the
screened group (Table 1, Table 3). To ensure our learning effects were not driven by
impulsivity, ANCOVA was conducted (with an interaction term between BIS-11 and
DRD4 status) on performance measures and model parameters. Results replicated for
overall accuracy, (p=.058), and accuracy for similar exemplars close to the prototypes,
(p=.04). This pattern of effects held both when including race as a covariate as well as
within a subset of Caucasian participants (NGeneral = 115, NSelected = 122) for all significant
analyses (Table 4).
Block 2: Screened Sample Only
Accuracy
DRD4 long allele carriers (M = .75, SD = .09) were significantly more accurate
overall at categorizing exemplars than short homozygotes, (M = .71, SD = .12), t(199) =
2.05, p=.04, η2 = .02, (Figure 3A). To assess whether accuracy effects differed as a
function of similarity to the prototype, we conducted a 2 DRD4 (long carrier, short
homozygote) X 2 similarity to prototype (similar, dissimilar) repeated measures ANOVA
(Figure 3B, 3C). There was a significant main effect of DRD4, F(1, 199) =3.96 , p=.05,
η2 = .01, where long carriers were more accurate than short homozygotes, and a
significant main effect of similarity , F(1, 199) =249.84 , p<.001, η2 = .28, where
accuracy was higher for exemplars that were more similar to the prototype (M = .8, SD =
.15) than those that were dissimilar (M = .63 , SD = .12 ).
Computational Modeling: Salience and Attention
To determine whether attentional focus for salient and minor features depends on
DRD4 allele status after experience, we conducted a 2 DRD4 (long carrier, short
homozygote) X 2 Feature (salient, minor) mixed effects ANOVA for block 2. There was
a significant main effect of Feature, F(1, 199) = 40.20, p<.001, η2 = .11, qualified by a
significant interaction between Feature and DRD4 status where long carriers have more
polarized attention than short homozygotes, F(1, 199) = 4.23, p=.04, η2 = .01. To
decompose the effects we conducted independent samples t-tests within each feature type
RUNNING HEAD: DRD4, PRIORITY ITEMS, AND ATTENTION
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between genetic groups. Though non-significant, long allele carriers demonstrated some
evidence of heightened attention for salient features (MLong Carrier = .22, SD = .23,
MShort=.16, SD =.20; Figure 3E), t(199) = 2.51, p=.13, η2 = .01, and significantly impaired
attention for minor features, t(199) = 2.18, p=.03, η2 = .02, relative to short homozygotes
(MLong Carrier = .03, SD = .03, MShort=.06, SD =.09; Figure 3D). To verify that narrow
attentional weight is important during reflective category learning, maximum attention
weight was correlated with prototype accuracy across groups. Narrow attention was
positively correlated with accuracy, r(199) = .21, p=.002.
Potential Confounds
Analysis of individual differences in impulsivity as measured by the BIS-11 was
significantly greater for long allele carriers relative to short homozygotes, t(190) = 3.31,
p=.001, with no other significant differences in personality or neuropsychological
variables at the p<.05 level (Table 1, Table 3). To ensure our learning effects were not
driven by impulsivity, ANCOVA was conducted (with an interaction term between BIS11 and DRD4 status) on performance measures and model parameters. DRD4’s effect on
overall accuracy (p=.04), similar exemplars (p=.03), and minor weight (p=.04) remained
significant. These effects held both when including race as a covariate as well as within a
subset of Caucasian participants (N = 124) for all analyses (Table 4).
Memory Span Task
Examining scores for the traditional OSPAN measure where errors result in a
span score of 0, long allele carriers scored significantly higher (M=50.00, SD=12.97)
than short homozygotes (M=41.94, SD=16.31), t(191)=2.63, p=.009, η2 = .04 (Figure
4A). The proportion of correctly recalled letters was also significantly higher for long
allele carriers (M=.85, SD=.15) than for short homozygotes (M=.80, SD=.08),
t(191)=2.09, p=.04, η2 = .02 (Figure 4B).
Potential Confounds
Other studies that have examined the OSPAN task have found that performance is
dependent on two dissociable processes – the interplay of executive attention and
working memory during updating and short-term memory capacity (Conway et al., 2005).
An examination of Table 3 indicates that there are no significant differences in multiple
measures of memory between DRD4 polymorphisms (working memory, short term
memory, episodic memory). This indicates that memory differences alone are not driving
these effects. Further, during the digit span task participants are asked to remember
sequences of 3-9 numbers without interference from a secondary task. The DRD4 long
allele advantage is absent in the absence of primary and secondary priorities as seen in
the OSPAN task.
Analysis of individual differences in impulsivity as measured by the BIS-11 was
significantly greater for long allele carriers relative to short homozygotes, t(190) = 3.31,
p=.008, with no other significant differences in personality or neuropsychological
variables at the p<.05 level (Table 1, Table 3). To ensure our learning effects were not
driven by impulsivity, ANCOVA was conducted (with an interaction term between BIS11 and DRD4 status) on performance measures and model parameters. DRD4’s effect on
OSPAN score (p=.008), and overall accuracy (p=.03) remained significant. These effects
held both when including race as a covariate as well as within a subset of Caucasian
participants (N = 118) for all analyses (Table 4).
Discussion
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Prior findings on the role of DRD4 in modulating attention have been mixed, with
some work finding a link between the long allele and deficits in attention, and other work
finding a link between the long allele and heightened attention to contextually relevant
information such as addictive cues. We tested a recently developed theory that suggests
that DRD4 serves as a ‘plasticity’ gene, rather than a ‘risk’ gene, where the long allele is
associated with heightened attention to goal-directed high-priority items and impaired
attention to low-priority items in the environment. Under this hypothesis heightened
attention to high-priority items biases cognitive resources resulting in greater plasticity in
learning and memory for high-priority items and reduced plasticity for low-priority items.
Thus, though the long allele is generally characterized as a deleterious polymorphism, if
high-priority items are critical for performance we expect long allele carriers to
outperform short homozygotes. Given the predominant transcription of DRD4 in
prefrontal areas (Oak et al., 2000), we propose that differences will be driven by goal
directed processes.
In the present study, we examined two domains of cognition where task
performance was supported by greater attention to either goal-related salient features in a
category-learning task or goal-related primary items in a sequential memory task. Long
allele carriers were better able to attend to these high-priority stimuli, and as a result
outperformed short allele homozygotes in both tasks. Long allele carriers also
demonstrated impaired attention for minor features which was not related to performance
in these tasks. Importantly, this advantage generalized in two cognitive domains learning and memory.
In addition, these studies examine both immediate (exemplar categorization) and
long run (sequential memory) goal-directed processes. During category learning, each
exemplar was processed alone without secondary distraction; however, each stimulus was
complex and had many perceptual features that were more or less salient. Immediate
focus on a small number of features was critical to create verbal rules that described the
category structure. The benefits of carrying the long allele are apparent during early
learning where accuracy is greater across a screened sample and a more general
replication sample. Interestingly, genetic differences in attention become apparent with
experience. In line with our plasticity hypothesis, computational models indicated that
screened long allele carriers had biased attention to high-priority salient features at the
expense of low-priority minor features relative to short homozygotes in the second block
of learning. Thus, long allele carriers’ greater plasticity during learning was due
exaggerated biased competition for priority items. Interestingly, impaired attention to
minor features may help explain attentional deficits that are sometimes seen in long allele
carriers, particularly those with ADHD (Bidwell et al., 2011; Kegel & Bus, 2012).
During the OSPAN memory task, on the other hand, priority items must be
maintained across 3 to 7 trials in the face of interference from a distracting secondary
task. The OSPAN task has been extensively used to measure working memory capacity,
which has been strongly linked with executive attention (Engle, 2002). A distinction has
often been made in the literature between working memory capacity, or executive
attention, and short-term working memory span. Working memory and executive
attention require maintenance of high-priority items in memory in the face of
interference, while short-term working memory refers to basic memory processes like
simply recalling a span of digits in order in the Digit Span task. We observed a strong
RUNNING HEAD: DRD4, PRIORITY ITEMS, AND ATTENTION
11
effect of DRD4 in the OSPAN task, a task that measures working memory capacity and
executive attention, but not in the Digit Span task, which measures only short-term
memory ability. This suggests that our findings were due to differences in executive
attention, rather than differences in short-term memory. Future work should extend these
findings into other cognitive domains such as decision-making and other categorylearning tasks to further examine the role of DRD4 in attention, learning, and memoryrelated processes.
It is important to note that heightened attention to high-priority items in the
environment may explain findings from several studies that have found heightened
cravings for addictive substances in DRD4 long allele carriers (reviewed in McGeary,
2009). Such cravings may result from heightened attention toward fulfilling the goal of
using the desired substances. Additionally, our findings may be relevant to work that has
shown an association between ADHD and prolonged video game and internet use (Chan
& Rabinowitz, 2006). Children with ADHD may have trouble focusing on stimuli that
do not seem contextually relevant or interesting (i.e. ‘boring’) or that are not tied to goalrelated processes, but may show heightened attention to stimuli that are interesting or
engaging like video games.
It is also interesting to note that long allele carriers have significantly higher
impulsivity than short homozygotes as measured by the BIS-11. This might suggest that
the impulsivity personality trait heightens attention to dominant items and reduces
attention to minor items and is driving our effects. However, in our analysis, advantages
in both category learning and memory spans in long allele carriers were robust when
controlling for impulsivity. Under our hypothesis, we speculate that differences in
impulsive behavior might emerge when long allele carriers are drawn more strongly to
novel items, which have been demonstrated to have higher-priority than mundane items
(Itti & Baldi, 2006; Ranganath & Rainer, 2003). Together these findings lend support to
prior literature that has linked DRD4 with increased impulsivity or novelty seeking but
does not support the relationship with behavior (Gizer & Waldman, 2012; Laucht et al.,
2005; Munafò et al., 2008; Ray et al., 2009). However, the literature is still mixed and
more work needs to be done to determine whether effects of impulsivity and novelty
seeking mediate behavioral outcomes in long allele carriers and how clinical symptoms
interact with these effects.
As with all genetic association studies, the associations reported here may be
driven by an unmeasured third variable (including but not limited to; another genetic
variant in linkage disequilibrium with DRD4 exon III, population stratification, or
environmental influences). The inclusion of ancestry informative markers could help
reduce this threat (Kosoy et al., 2009). Future directions that manipulate the
dopaminergic system through pharmacology or dietary depletion may provide supporting
evidence for these findings (e.g., use of a medication with D4 properties such as
olanzapine to lend credence to initial association findings: Hutchison et al 2003).
Similarly, more comprehensive molecular characterization of the DRD4 and other genes
within the candidate biological system may increase confidence in and understanding of
these relationships. Finally, given the departure from Hardy Weinberg Equilibrium
(HWE), the sample recruited for this study may not represent a random sample of the
population and therefore the results should be considered with caution. There are a
number of situations that may lead to departures from HWE (e.g., genotyping error,
RUNNING HEAD: DRD4, PRIORITY ITEMS, AND ATTENTION
12
nonrandom mating, selected samples, etc.) and it is not clear which factor may be
responsible. Genotyping error is highly unlikely given the quality control checks in place
and numerous other samples genotyped by this lab that have not deviated from HWE
(genotyped for this same polymorphism). Further, the sample for this study was recruited
from the community and was not selected on any phenotype that would drive such
results, thus it is unlikely that the deviation from HWE is due to sample selection.
Though we do demonstrate attentional impairments for minor items in long allele
carriers, another limitation of the current study is that it does not demonstrate impaired
performance during a task where attention to high-priority items is detrimental. Future
research would do well to examine whether long allele carriers demonstrate heightened
attention to priority features and impaired attention to minor features when in tasks that
are both supported and impaired by this kind of attention to priority features. Finally,
despite theoretically consistent findings across two studies that are based on biologicallysupported hypotheses, replication of these relationships in separate larger samples is
warranted.
Conclusions
The DRD4 long allele has been positively selected which suggests that it likely
confers some type of adaptive advantage (Ding et al., 2002; Oak et al., 2000). While the
long allele has been associated with attentional disorders in prior work (Bidwell et al.,
2011; Gizer et al., 2009; Gizer & Waldman, 2012; Hutchison et al., 2003; Kustanovich et
al., 2003; Langley et al., 2004; Laucht et al., 2005; 2007; Manor et al., 2001; Munafò et
al., 2008; Swanson et al., 2000), our results add to a growing body of research that
suggests that the presence of this allele may confer advantages in certain situations, and
may interact with the environment and other contextual and goal-related factors to
modulate goal-directed attention. Future work should continue to examine whether this
gene does indeed serve as a plasticity gene, rather than one that simply confers risk, by
considering the important interplay between genetics and environment in modulating
attention.
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Figures
Figure 1. A schematic of the Category Learning and Memory Span tasks. A) The
category structure and B) the task timeline for the Category Learning task. Participants
completed 1 (general sample) or 2 (screened sample) blocks of 20 training trials with
corrective feedback and 42 test trials. C) A diagram of the Operation Span Task
(OSPAN). Participants completed 15 trials of three to seven memory spans. During each
span, participants indicated whether math problems were correct while remembering an
alternating sequence letters. After three to seven math-letter sequences there was a free
recall phase where participants were asked to recall the letters in order.
RUNNING HEAD: DRD4, PRIORITY ITEMS, AND ATTENTION
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Figure 2. Plots of the results from block 1 of the Category Learning task (Nscreened = 198;
Ngeneral =187). A) Category learning overall accuracy by DRD4 status, B) similar
exemplar accuracy, and C) dissimilar exemplar accuracy by DRD4 status, as well as the
mean attention weight for D) the most minor and E) the most salient category dimension.
Standard error bars included.
RUNNING HEAD: DRD4, PRIORITY ITEMS, AND ATTENTION
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Figure 3. Plots of the results from block 2 of the Category Learning task (N =201). A)
Category learning overall accuracy, B) similar exemplar accuracy, and C) dissimilar
exemplar accuracy by DRD4 status, the mean attention weight for D) the most minor and
E) the most salient category dimension are shown by DRD4 status. Standard error bars
included.
RUNNING HEAD: DRD4, PRIORITY ITEMS, AND ATTENTION
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Figure 4. Behavioral results from the Memory Span task (OSPAN; N = 193). A) OSPAN
scores and B) OSPAN overall accuracy are shown by DRD4 status. Standard error bars
included.
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Table 1.
Demographics and Individual Differences information for Category Learning
and Memory Span tasks.
Category Learning
General Sample
Age
Years of Education
Gender
BIS-11†
CESD
Ethnicity
Hispanic
Non-Hispanic
Decline
Race
Caucasian
Asian
Decline
Other
Long Carriers
19.29 (2.28)
12.93 (1.54)
F = 32; M = 17
65.67 (8.51)
17.69 (10.55)
Short Homozygotes
19.33 (2.66)
12.94 (1.35)
F = 95; M = 43
62.96 (8.45)
15.91 (9)
17
32
0
24
112
2
36
2
2
9
79
40
1
18
Category Learning
Screened Sample
Age
Years of Education
Gender
BIS-11**
CESD†
Ethnicity
Hispanic
Non-Hispanic
Decline
Race
Caucasian
Asian
Decline
Other
Long Carriers
24.77 (4.44)
15.68 (2.63)
F = 20; M = 11
69.33(4.82)
6.3(5.17)
Short Homozygotes
24.88 (4.5)
15.23 (2.27)
F = 93; M = 77
72.55(4.9)
8.71(6.54)
8
20
3
34
132
4
22
1
3
5
102
40
7
21
RUNNING HEAD: DRD4, PRIORITY ITEMS, AND ATTENTION
Memory Span
Long Carriers
24.66 (4.42)
15.59 (2.55)
F = 19; M = 13
69.68 (4.67)
6.81 (5.24)
Short Homozygotes
24.81 (4.44)
15.33 (2.31)
F = 97; M = 64
72.12 (4.76)
9.03 (6.73)
Age
Years of Education
Gender
BIS-11**
CESD†
Ethnicity
Hispanic
7
31
Non-Hispanic
22
129
Decline
3
1
Race
Caucasian
23
95
Asian
2
38
Decline
2
7
Other
5
21
Note: Standard deviations are listed in parentheses where appropriate.
*Significant at p<.05, **significant at p<.01, ***significant at p<.001,
†marginally significant at p<.10.
21
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Table 2. Number of tandem repeats in exon III for each allele of DRD4. Grey cells are
Short Homozygotes (less than 6 tandem repeats for both alleles) and white cells are Long
Allele Carriers (at least one allele with 7 or more tandem repeats).
Category Learning: General Sample
1
2
3
4
5
6
7
8
9
10
1
0
0
0
0
0
0
0
0
0
0
2
0
9
0
0
0
0
0
0
0
0
3
0
1
2
0
0
0
0
0
0
0
4
0
18
3
98
0
0
0
0
0
0
5
0
0
0
6
0
0
0
0
0
0
6
0
0
0
0
0
1
0
0
0
0
7
0
4
1
30
2
3
3
0
0
0
8
0
2
0
2
0
0
0
1
0
0
9
0
0
0
1
0
0
0
0
0
0
10
0
0
0
0
0
0
0
0
0
0
11
0
0
0
0
0
0
0
0
0
0
138 = N Short Homozygote (73.8%); 49 = N Long Allele Carrier (26.2%)
11
0
0
0
0
0
0
0
0
0
0
0
Category Learning: Screened Sample
1
2
3
4
5
6
7
8
9
10
1
0
0
0
0
0
0
0
0
0
0
2
0
22
0
0
0
0
0
0
0
0
3
0
4
1
0
0
0
0
0
0
0
4
0
29
5
99
0
0
0
0
0
0
5
0
0
1
3
1
0
0
0
0
0
6
0
1
1
3
0
0
0
0
0
0
7
0
3
1
17
0
1
6
0
0
0
8
0
1
0
0
0
0
0
0
0
0
9
0
0
0
0
0
0
0
0
0
0
10
0
1
0
1
0
0
0
0
0
0
11
0
0
0
0
0
0
0
0
0
0
170 = N Short Homozygote (84.6%); 31 = N Long Allele Carrier (15.4%)
11
0
0
0
0
0
0
0
0
0
0
0
Memory Span
1
2
1
0
0
2
0
22
3
0
5
3
0
0
0
4
0
0
0
5
0
0
0
6
0
0
0
7
0
0
0
8
0
0
0
9
0
0
0
10
0
0
0
11
0
0
0
RUNNING HEAD: DRD4, PRIORITY ITEMS, AND ATTENTION
4
0
26
4
95
0
0
0
0
0
0
5
0
0
1
2
1
0
0
0
0
0
6
0
1
1
3
0
0
0
0
0
0
7
0
3
1
17
0
1
6
0
0
0
8
0
1
0
1
0
0
0
0
0
0
9
0
0
0
0
0
0
0
0
0
0
10
0
1
0
1
0
0
0
0
0
0
11
0
0
0
0
0
0
0
0
0
0
161 = N Short Homozygote (83.4%); 32 = N Long Allele Carrier (16.6%)
23
0
0
0
0
0
0
0
0
RUNNING HEAD: DRD4, PRIORITY ITEMS, AND ATTENTION
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Table 3.
DRD4 Long Carriers and Short Homozygotes Neuropsychological Tests for Category Learning and Memory
Category Learning: Screened Sample
Digit Span
WAIS Vocabulary†
Stroop Interference
WMSIII Delayed Recall
WMSIII Immediate Recall
Composite Memory
Memory Span
Digit Span†
WAIS Vocabulary†
Stroop Interference†
WMSIII Delayed Recall
WMSIII Immediate Recall
Composite Memory†
Note: Composite Memory is the average z-scores across the three memory tests; Digit Span, WMSIII Delaye
RUNNING HEAD: DRD4, PRIORITY ITEMS, AND ATTENTION
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Table 4: Average dependent measures for the Caucasian subset of DRD4 Long Carriers
and Short Homozygotes for the Category Learning and Memory Span tasks.
Category Learning: Block 1
General Sample
Long Carriers
Overall Accuracy
0.7 (0.13)
Similar Exemplar Accuracy
0.77 (0.18)
Dissimilar Exemplar Accuracy
0.61 (0.11)
Salient Feature Weight
0.04 (0.06)
Minor Feature Weight
0.19 (0.4)
Short Homozygotes
0.68 (0.17)
0.74 (0.21)
0.61 (0.15)
0.06 (0.07)
0.18 (0.38)
Screened Sample
Long Carriers
Overall Accuracy
0.71 (0.16)
Similar Exemplar Accuracy
0.77 (0.2)
Dissimilar Exemplar Accuracy
0.63 (0.15)
Salient Feature Weight
0.05 (0.1)
Minor Feature Weight
0.13 (0.34)
Short Homozygotes
0.7 (0.13)
0.76 (0.17)
0.63 (0.13)
0.05 (0.07)
0.19 (0.4)
Category Learning: Block 2
Screened Sample
Long Carriers
Overall Accuracy*
0.78 (0.1)
Similar Exemplar Accuracy*
0.86 (0.13)
Dissimilar Exemplar Accuracy
0.67 (0.12)
Salient Feature Weight
0.19 (0.2)
Minor Feature Weight*
0.03 (0.04)
Short Homozygotes
0.71 (0.12)
0.79 (0.15)
0.62 (0.13)
0.15 (0.19)
0.07 (0.09)
Memory Span
Long Carriers
Short Homozygotes
Score**
50 (12.98)
41.94 (16.31)
Proportion Correct*
0.85 (0.09)
0.8 (0.15)
Note: Standard deviations are listed in parentheses. Independent samples t-tests
*Significant at p<.05, **significant at p<.01, ***significant at p<.001, †marginally
significant at p<.10.
RUNNING HEAD: DRD4, PRIORITY ITEMS, AND ATTENTION
Equations
Equation 1:
di P = å wk ( xik - Pki )
Equation 2:
hi P = e-cd P
i
Equation 3:
P(A | Si ) =
hi PA
hi PA + hi PB
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